Sharp Bounds on Functionals of the Joint Distribution in the Analysis of Treatment Effects
提出一种识别和估计方法，用于界定处理效应文献中潜在结果联合分布的连续泛函，适用于无处理选择限制的模型，可处理有工具变量和额外约束的情况，并讨论了计算问题。
This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate additional model constraints. However, computational considerations for the method are found to be important and are discussed in detail. Supplementary materials for this article are available online.